A key factor in successfully achieving remarkable performance for deep learning models is the availability of large amounts of data. However, in scientific visualization, providing such extensive volumetric data is often infeasible due to the high computational cost of simulations and the challenges of data storage. To address this data sparsity issue in model training, we propose a few-shot learning framework that leverages only few training samples (e.g., 1, 3, or 5) to ensure both generalization capability and performance through a conditional diffusion model. Our approach consists of two stages: the forward process and the reverse process. In the forward process, we inject noise at various levels into the few samples. In the reverse process, we design a time-aware UNet that iteratively learns to denoise the noisy data. Additionally, we introduce a noise-aware loss function that dynamically adjusts optimization weights based on the noise levels in the training data. Our method demonstrates consistent and robust performance, regardless of the selection method for the few training samples. Furthermore, it achieves superior results in both quantitative and qualitative evaluations compared to state-of-the-art solutions across three scientific visualization tasks: spatial super-resolution, temporal super-resolution, and variable translation.
Jun Han (Thu,) studied this question.